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An introduction to statistical learning : with Applications in Python / Gareth James ... [et al.]
| An introduction to statistical learning : with Applications in Python / Gareth James ... [et al.] |
| Pubbl/distr/stampa | Cham, : Springer, 2023 |
| Descrizione fisica | xv, 60 p. : ill. ; 24 cm |
| Soggetto non controllato |
Data Mining
Inference Python Python software Statistical learning Supervised learning Unsupervsied learning |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0278708 |
| Cham, : Springer, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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An introduction to statistics with Python : with applications in the life sciences / Thomas Haslwanter
| An introduction to statistics with Python : with applications in the life sciences / Thomas Haslwanter |
| Autore | Haslwanter, Thomas |
| Edizione | [2. ed] |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | xvi, 336 p. : ill. ; 24 cm |
| Soggetto non controllato |
Alternative to R
Applications in the life sciences Bayesian Statistics Data Visualization Data analysis Generalized Linear Models Hypothesis tests Introductory Statistics Patterns in data Programming tools Python Python source code Regression Statistical Methods Statistical Modelling Statistical tests Survival times Time series |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0276849 |
Haslwanter, Thomas
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| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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An introduction to statistics with Python : with applications in the life sciences / Thomas Haslwanter
| An introduction to statistics with Python : with applications in the life sciences / Thomas Haslwanter |
| Autore | Haslwanter, Thomas |
| Pubbl/distr/stampa | [Cham], : Springer, 2016 |
| Descrizione fisica | XVII, 278 p. : ill. ; 24 cm |
| Soggetto topico |
92-XX - Biology and other natural sciences [MSC 2020]
62-XX - Statistics [MSC 2020] 62F15 - Bayesian inference [MSC 2020] 62F03 - Parametric hypothesis testing [MSC 2020] 62P10 - Applications of statistics to biology and medical sciences; meta analysis [MSC 2020] 92B15 - General Biostatistics [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] 62H15 - Hypothesis testing in multivariate analysis [MSC 2020] 62F40 - Bootstrap, jackknife and other resampling methods [MSC 2020] 62N02 - Estimation in survival analysis and censored data [MSC 2020] 62N03 - Testing in survival analysis and censored data [MSC 2020] 68T09 - Computational aspects of data analysis and big data [MSC 2020] |
| Soggetto non controllato |
Alternative to R
Applications in life sciences Data analysis Introductory Statistics Programming Python Python source code Statistical Methods Statistical tests Statistics and computing |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0114408 |
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Haslwanter, Thomas
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| [Cham], : Springer, 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Applied Time Series Analysis and Forecasting with Python / Changquan Huang, Alla Petukhina
| Applied Time Series Analysis and Forecasting with Python / Changquan Huang, Alla Petukhina |
| Autore | Huang, Changquan |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | x, 372 p. : ill. ; 24 cm |
| Altri autori (Persone) | Petukhina, Alla |
| Soggetto non controllato |
Artificial Intelligence
Big data analysis Data Visualization Data science Financial Time Series Forecasting Machine Learning for Time Series Markov switching models Multivariate time series Nonstationary Time Series Python State-space models Stationary Time Series Time Series Analysis |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0276890 |
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Huang, Changquan
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| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Bayesian Statistical Modeling with Stan, R, and Python / Kentaro Matsuura
| Bayesian Statistical Modeling with Stan, R, and Python / Kentaro Matsuura |
| Autore | Matsuura, Kentaro |
| Pubbl/distr/stampa | Singapore, : Springer, 2022 |
| Descrizione fisica | xix, 385 p. : ill. ; 24 cm |
| Soggetto non controllato |
Bayesian Modeling
Python Stan Statistical modeling |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0278348 |
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Matsuura, Kentaro
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| Singapore, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Computational Frameworks for Political and Social Research with Python / Josh Cutler, Matt Dickenson
| Computational Frameworks for Political and Social Research with Python / Josh Cutler, Matt Dickenson |
| Autore | Cutler, Josh |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xv, 209 p. : ill. ; 24 cm |
| Altri autori (Persone) | Dickenson, Matt |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
91-XX - Game theory, economics, finance, and other social and behavioral sciences [MSC 2020] |
| Soggetto non controllato |
Application programming
Computer vision Data Collection Data structures Databases Natural Language Processing Political analysis Programming Python |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0248837 |
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Cutler, Josh
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| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Entropy in Image Analysis II
| Entropy in Image Analysis II |
| Autore | Sparavigna Amelia Carolina |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (394 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
image binarization
optical character recognition local entropy filter thresholding image preprocessing image entropy image encryption medical color images RGB chaotic system crowd behavior analysis salient crowd motion detection repulsive force direction entropy node strength Pompe disease children quantitative muscle ultrasound texture-feature parametric imaging compound chaotic system S-box image information entropy image chaotic encryption cryptography Latin cube bit cube chosen plaintext attack atmosphere background engine flame infrared radiation detectability image quality evaluation image retrieval pooling method convolutional neural network feature distribution entropy lossless compression pattern classification machine learning malaria infection entropy Golomb–Rice codes image processing image segmentation weld segmentation weld evaluation convolution neural network Python Keras RSNNS MXNet brain-computer interface (BCI) electroencephalography (EEG) motor imagery (MI) continuous wavelet transform (CWT) convolutional neural network (CNN) hyperchaotic system filtering DNA computing diffusion deep neural network data expansion blind image quality assessment saliency and distortion human visual system declining quality data hiding AMBTC steganography stego image dictionary-based coding pixel value adjusting neuroaesthetics symmetry balance complexity chiaroscuro normalized entropy renaissance portrait paintings art history art statistics chaotic systems DNA coding security analysis magnetic resonance images non-maximum suppression object detection key-point detection IoU feature fusion quasi-resonant Rossby/drift wave triads Mordell elliptic curve pseudo-random numbers substitution box nuclear spin generator medical image peak signal-to-noise ratio key space calculation Duchenne muscular dystrophy ultrasound backscattered signals medical imaging neural engineering computer vision crowd motion detection security |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557433403321 |
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Sparavigna Amelia Carolina
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Finite Difference Computing with PDEs : A Modern Software Approach / Hans Petter Langtangen, Svein Linge
| Finite Difference Computing with PDEs : A Modern Software Approach / Hans Petter Langtangen, Svein Linge |
| Autore | Langtangen, Hans P. |
| Pubbl/distr/stampa | Cham, : SpringerOpen, 2017 |
| Descrizione fisica | xxiii, 499 p. : ill. ; 24 cm |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
35-XX - Partial differential equations [MSC 2020] 65-XX - Numerical analysis [MSC 2020] 34-XX - Ordinary differential equations [MSC 2020] |
| Soggetto non controllato |
Differential equations
Finite-difference methods Numerical methods Open Access Programming Python Verification |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0123901 |
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Langtangen, Hans P.
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| Cham, : SpringerOpen, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Introduction to Numerical Methods for Variational Problems / Hans Petter Langtangen, Kent-Andre Mardal
| Introduction to Numerical Methods for Variational Problems / Hans Petter Langtangen, Kent-Andre Mardal |
| Autore | Langtangen, Hans P. |
| Pubbl/distr/stampa | Cham, : Springer, 2019 |
| Descrizione fisica | xvi, 386 p. : ill. ; 24 cm |
| Altri autori (Persone) | Mardal, Kent-André |
| Soggetto topico |
65Kxx - Numerical methods for mathematical programming, optimization and variational techniques [MSC 2020]
35R10 - Functional partial differential equations [MSC 2020] 74Sxx - Numerical and other methods in solid mechanics [MSC 2020] 68N01 - General topics in the theory of software [MSC 2020] |
| Soggetto non controllato |
Computational modeling
Finite element methods Python Scientific Computing Scripting Variational methods |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0126933 |
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Langtangen, Hans P.
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| Cham, : Springer, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Introduction to Scientific Programming with Python / Joakim Sundnes
| Introduction to Scientific Programming with Python / Joakim Sundnes |
| Autore | Sundnes, Joakim |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xiv, 148 p. : ill. ; 24 cm |
| Soggetto topico |
65-XX - Numerical analysis [MSC 2020]
00A06 - Mathematics for nonmathematicians (engineering, social sciences, etc.) [MSC 2020] 68N15 - Theory of programming languages [MSC 2020] |
| Soggetto non controllato |
Computational science
Data science Object-oriented programming Programming Python |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0249367 |
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Sundnes, Joakim
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| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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